@RAVerBruggen Q: To address multicollinearity (even mild) why isn't it standard to do this--remove variable of interest, use residuals?
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Replying to @RAVerBruggen
@RAVerBruggen When I do that, coefficient falls quite a bit:pic.twitter.com/KqytN8maJ5
2 replies 0 retweets 0 likes -
Replying to @RAVerBruggen
@RAVerBruggen The coefficients are not very interpretable because they are not on the same scale. You need to standardize the data.2 replies 0 retweets 0 likes -
Replying to @KirkegaardEmil
@KirkegaardEmil You mean the residuals of a model are on a different scale than the original variable?2 replies 0 retweets 0 likes -
Replying to @RAVerBruggen
@RAVerBruggen The variables in your orig. model are also not on the same scale, so beta for one not comparable to beta for another.1 reply 0 retweets 0 likes -
Replying to @KirkegaardEmil
@KirkegaardEmil Right, but I'm just concerned about 1 percentage point change in gun ownership = X percent change in Murder1 reply 0 retweets 0 likes -
Replying to @RAVerBruggen
@KirkegaardEmil That's 2.2 percent in the original model. It's not about 1 percent with the residuals?1 reply 0 retweets 0 likes -
Replying to @RAVerBruggen
@KirkegaardEmil Journalism major, but I thought residuals were the difference between model prediction & actual value. How different scale?1 reply 0 retweets 0 likes -
Replying to @RAVerBruggen
@KirkegaardEmil (So if model says murder's 4, actual rate is 5, the residual is 1. Both are murders/100K. Do I completely misunderstand?)1 reply 0 retweets 0 likes -
Replying to @RAVerBruggen
@RAVerBruggen Residuals also take into account the intercept.2 replies 0 retweets 0 likes
@RAVerBruggen I prefer not to use Twitter as an IM. We can discuss it more, but then install TOX and add me instead. ;)
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